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On the equivalence of confidence interval estimation based on frequentist model averagingand least-squares for the full model in linear regression
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2016 (English)Report (Other academic)
Abstract [en]

In many applications of linear regression models, model selection is vital. However, randomness due to model selection is commonly ignored in post-model selection inference. In order to account for the model selection uncertainty in these linear models, least squares frequentist model averaging has been proposed recently. In this paper, we show that the confidence interval from model averaging is asymptotically equivalent to the confidence interval from the full model. Furthermore, we demonstrate that this equivalence also holds in finite samples if the parameter of interest is a linear function of the regression coefficients.

Place, publisher, year, edition, pages
2016. , 13 p.
Series
Working paper / Department of Statistics, Uppsala University, 2016:1
Keyword [en]
Asymptotic equivalence; Linear model; Local asymptotics; Model selection uncertainty; Post-selection inference
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:uu:diva-283707OAI: oai:DiVA.org:uu-283707DiVA: diva2:919537
Available from: 2016-04-14 Created: 2016-04-14 Last updated: 2016-04-14Bibliographically approved

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Ankargren, SebastianJin, Shaobo

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf